<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Science on Aneesh Sathe</title><link>https://aneeshsathe.com/tags/science/</link><description>Recent content in Science on Aneesh Sathe</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 29 Jul 2025 06:04:54 +0000</lastBuildDate><atom:link href="https://aneeshsathe.com/tags/science/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Every Biotech Research Group Needs a Data Lakehouse</title><link>https://aneeshsathe.com/why-every-biotech-research-group-needs-a-data-lakehouse/</link><pubDate>Tue, 29 Jul 2025 06:04:54 +0000</pubDate><guid>https://aneeshsathe.com/why-every-biotech-research-group-needs-a-data-lakehouse/</guid><description>&lt;p&gt;start tiny and scale fast without vendor lock-in&lt;/p&gt;
&lt;p&gt;All biotech labs have data, tons of it. The problem is the same across scales. Accessing data across experiments is hard. Often data simply gets lost on somebody’s laptop with a pretty plot on a poster as the only clue it ever existed. The problem is almost insurmountable if you try to track multiple data types. Trying to run any kind of data management activity used to have large overhead. New technology like DuckDB and their new data lakehouse infrastructure, DuckLake, try to make it very easy to adopt and scale with your data. All while avoiding vendor lock-in.&lt;/p&gt;</description></item><item><title>My Road to Bayesian Stats</title><link>https://aneeshsathe.com/my-road-to-bayesian-stats/</link><pubDate>Tue, 22 Jul 2025 06:32:55 +0000</pubDate><guid>https://aneeshsathe.com/my-road-to-bayesian-stats/</guid><description>&lt;p&gt;By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought.&lt;/p&gt;</description></item><item><title>Divine Documentation</title><link>https://aneeshsathe.com/divine-documentation/</link><pubDate>Wed, 16 Jul 2025 05:20:46 +0000</pubDate><guid>https://aneeshsathe.com/divine-documentation/</guid><description>&lt;p&gt;Dad was about my age when he said that reading the manual was better than hypothesis driven button pressing. For teenage me, that took too long. Sure, I may have crashed a computer or two but following my gut got me there. Of course my gut isn&amp;rsquo;t &lt;em&gt;that&lt;/em&gt; smart. In the decades preceding, devices had converged on a common pattern language of buttons. Once learned, the standard grammar of action would reliably deliver me to my destination.&lt;/p&gt;</description></item><item><title>Beyond the Dataset</title><link>https://aneeshsathe.com/beyond-the-dataset/</link><pubDate>Fri, 11 Jul 2025 05:41:24 +0000</pubDate><guid>https://aneeshsathe.com/beyond-the-dataset/</guid><description>&lt;p&gt;On the recent season of the show Clarkson’s farm, J.C. goes through great lengths to buy the right pub. As with any sensible buyer, the team does a thorough tear down followed by a big build up before the place is open for business. They survey how the place is built, located, and accessed. In their refresh they ensure that each part of the pub is built with purpose. Even the tractor on the ceiling. The art is  in answering the question: &lt;em&gt;How was this place put together?&lt;/em&gt;&lt;/p&gt;</description></item></channel></rss>